| 重庆市县域农村贫困化空间分异及影响因素探析 |
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| 引用本文： | 李涛,张帅,廖和平.重庆市县域农村贫困化空间分异及影响因素探析[J].西南农业大学学报,2020,42(4):1-11. |
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| 作者姓名： | 李涛 张帅 廖和平 |
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| 作者单位： | 1. 西南大学 地理科学学院, 重庆 400715;2. 西南大学 精准扶贫与区域发展评估研究中心, 重庆 400715 |
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| 基金项目： | 重庆市社会科学规划项目（2018BS86）；西南大学博士启动基金项目（swu118047）；教育部人文社会科学研究青年基金项目（20XJCZH005）. |
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| 摘 要： | 以重庆市为研究区,使用重庆市涉及扶贫开发任务的33个区县2014—2018年县域农村贫困数据,采用探索性空间数据分析(ESDA),刻画农村贫困化空间关联格局,构建影响县域农村贫困化空间分异的指标体系,并运用地理加权回归模型对县域农村贫困化的空间分异影响因素进行回归分析和探讨.研究结果表明:①总体来看,研究时段内重庆市县域农村贫困化热点区域整体空间格局稳定,高值区主要分布于巫溪、城口、酉阳和彭水等渝东北和渝东南区域的相关区县,而主城区和渝西地区的区县贫困发生率相对较低,呈现显著的空间差异性;从时间序列上来看,研究时段内各区县农村贫困发生率明显呈现下降态势,区县间的差距在缩小;②重庆市2014-2018年全局Moran’s I指数均达到显著的空间正相关,县域农村贫困化呈现出明显的空间集聚现象;③通过GWR模型进行回归分析发现,县域农村贫困化空间分异影响因素中,海拔高度、人均耕地资源面积、 25°以上耕地面积比重和区位水平、未通客运班车村比重、农村居民人均纯收入、文盲人口比、因残致贫人口比重等8个显著的解释变量对县域农村贫困化起到了直接或间接的影响,且各影响因素在不同区域上表现出的影响力不尽相同.
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| 关 键 词： | 农村贫困 空间差异 地理加权回归 重庆市 |
| 收稿时间： | 2019/10/10 0:00:00 |
Analysis on the Spatial Differentiation and Influencing Factors of County-Level Rural Poverty in Chongqing |
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| LI Tao,ZHANG Shuai,LIAO He-ping.Analysis on the Spatial Differentiation and Influencing Factors of County-Level Rural Poverty in Chongqing[J].Journal of Southwest Agricultural University,2020,42(4):1-11. |
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| Authors: | LI Tao ZHANG Shuai LIAO He-ping |
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| Institution: | 1. School of Geographical Sciences, Southwest University, Chongqing 400715, China;2. Center for Targeted Poverty Alleviation and Regional Deveiopment Assessment, Southwest University, Chongqing 400715, China |
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| Abstract: | Based on the rural poverty data of 33 districts and counties in Chongqing from 2014 to 2018, this study uses exploratory spatial data analysis (ESDA) to portray the spatial relationship pattern of county-level rural poverty of the municipality. An index system of spatial differentiation of county-level rural poverty is constructed, and the geographically weighted regression (GWR) model is used in regression analysis and discussion of the influencing factors for the spatial differentiation factors of rural poverty. The results show that the overall spatial pattern of rural poverty-stricken hotspots in Chongqing is stable during the study period, and the high-value areas are mainly distributed in the northeast of Chongqing, such as Wuxi and Chengkou, and in the southeast, such as Youyang and Pengshui, and the incidence of poverty in the districts and counties in the western part of Chongqing and the main urban area is relatively low, thus showing significant spatial differences. From the time series, the incidence of rural poverty in all districts and counties during the study period is obviously declining. The gap between districts and counties is narrowing. In 2014-2018, Chongqing''s overall Moran''s I index reached a significant spatial positive correlation and the rural poverty in the counties/districts showed obvious spatial agglomeration. Regression analysis with the GWR model indicates that of all the factors affecting the spatial differentiation of county-level rural poverty, 8 significant explanatory variables (altitude, per-capita cultivated land area, the proportion and the location level of cultivated land above 25°, the proportion of passenger bus non-accessible villages, the per-capita net income of rural residents, the illiteracy rate, and disability-caused poverty) have a direct or indirect impact on county-level rural poverty. |
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| Keywords: | rural poverty spatial difference geographically weighted regression (GWR) Chongqing |
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